Asynchronous parameter aggregation for machine learning

ABSTRACT

Systems and methods are provided for training a machine learned model on a large number of devices, each device acquiring a local set of training data without sharing data sets across devices. The devices train the model on the respective device&#39;s set of training data. The devices communicate a parameter vector from the trained model asynchronously with a parameter server. The parameter server updates a master parameter vector and transmits the master parameter vector to the respective device.

FIELD

The following disclosure relates to location, navigation, and/or mappingservices.

BACKGROUND

Massive volumes of data are collected every day by large number ofdevices such as smartphones and navigation devices. The data includeseverything from user habits to images to speech and beyond. Analysis ofthe data could improve learning models and user experiences. Forexample, language models can improve speech recognition and text entry,and image models can help automatically identify photos.

The complex problem of training these models could be solved by largescale distributed computing by taking advantage of the resource storage,computing power, cycles, content, and bandwidth of participating devicesavailable at edges of a network. In such a distributed machine learningscenario, the dataset is transmitted to or stored among multiple edgedevices. The devices solve a distributed optimization problem tocollectively learn the underlying model. For distributed computing,similar (or identical) datasets may be allocated to multiple devicesthat are then able to solve a problem in parallel.

However, privacy and connectivity concerns may prohibit data from beingshared between devices preventing largescale distributed methods. Usersmay prefer to not share voice, video, or images with other devices orunknown users. Devices may not be simultaneously or continuouslyconnected and may contain disparate data sets. Bandwidth concerns mayprohibit timely sharing of data.

SUMMARY

In an embodiment, a navigation device is provided for training a machinelearned model. The device includes at least one sensor, a communicationsinterface, and a device processor. The at least one sensor is configuredto acquire training data. The communications interface is configured tocommunicate with a parameter server. The device processor is configuredto train the machine learned model using the training data, transmit aparameter vector of the trained model to the parameter server, andreceive in response, an updated central parameter vector from theparameter server. The device processor is further configured to retrainthe model using the updated central parameter vector. The navigationdevice acquires different training data from other devices that aretraining the model. The at least one transmission between the navigationdevice and the parameter server occurs asynchronously with respect tothe other devices that are training the model.

In an embodiment, a method is provided for training a machine learnedmodel using a plurality of distributed worker devices. A worker devicetrains a machine learned model using local training data and a set offirst parameters. The worker device transmits a set of second parametersof the trained machine learned model to a parameter server. The workerdevice receives a set of third parameters from the parameter server. Theset of third parameters is calculated at least partially as a functionof the set of second parameters. The worker device trains the machinelearned model using the local training data and the set of thirdparameters.

In an embodiment, a system is provided for training a machine learnedmodel. The system includes a plurality of worker devices and a parameterserver. The plurality of worker devices are configured to train themachine learned model using a set of parameters and respective sets oflocally acquired training data. The parameter server is configured tocommunicate with the plurality of worker devices. The parameter serveris configured to receive locally generated sets of parameters of thetrained machine learned models from the plurality of worker devices,calculate, and transmit, in response to a communication from a workerdevice of the plurality of worker devices, a set of central parametersto the respective worker device from which the communication originated.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention are described herein withreference to the following figures.

FIG. 1 depicts an example system for distributed asynchronous machinelearning according to an embodiment.

FIG. 2 depicts an example system for distributed asynchronous machinelearning according to an embodiment.

FIG. 3 depicts a workflow for distributed asynchronous machine learningaccording to an embodiment.

FIG. 4 depicts an example device for distributed asynchronous machinelearning according to an embodiment.

FIG. 5 depicts an example system for distributed asynchronous machinelearning according to an embodiment.

FIG. 6 depicts an example system for distributed asynchronous machinelearning according to an embodiment.

FIG. 7 depicts an example device of the system of FIG. 1 according to anembodiment.

FIG. 8 depicts an example map of a geographic region.

FIG. 9 depicts an example data structure of a geographic database.

DETAILED DESCRIPTION

Embodiments described herein provide systems and methods for training amachine learned model on a large number of devices. Each device acquiresa local set of training data without sharing data sets across devices.The devices train the model on the respective device's set of trainingdata. The devices communicate a parameter vector from the trained modelasynchronously with a parameter server. The parameter server updates amaster parameter vector and transmits the master parameter vector to therespective device. This process repeats multiple times, each devicetraining the local model to determine a parameter vector, transmittingthe parameter vector to the parameter server, receiving the updatedmaster parameter vector, and retraining the local model.

Training of models, e.g. machine learned networks, requires a largeamount of data. However, gathering and labeling this data may beprohibitive and expensive. Privacy concerns and bandwidth issues may notallow for gathering of such a large amount of data in a centralizedlocation. As described here within, machine learning provides fordevices to learn to iteratively identify a solution not known a priorior without being programmed explicitly to identify the solution. Machinelearning uses two types of techniques: supervised learning, which trainsa model on known input and output data so that the model may predictfuture outputs, and unsupervised learning, which finds hidden patternsor intrinsic structures in input data. Both techniques require largeamounts of data to “learn” to generate an accurate output.

Supervised machine learning teaches a model using a large known(labeled) set of data. The training method takes the labeled set andtrains a model to generate predictions for a response to new data. Themodel, in other words, is taught to recognize patterns (sometimescomplex) in labeled data and then applies the patterns to new data.Different techniques may be used for supervised learning including, forexample, classification, regression, and/or adversarial techniques.

Classification techniques predict discrete responses, for example,whether an email is genuine or spam, whether an image depicts a cat ordog, whether a tumor is cancerous or benign. Classification modelsclassify input data into categories. Some applications of classificationinclude object identification, medical imaging, speech recognition, andcredit scoring. Classification techniques may be used on data that canbe tagged, categorized, or separated into specific groups or classes.For example, applications for hand-writing recognition and imagerecognition use classification to recognize letters and numbers.Classification techniques may use optimization methods such as gradientdescent. Other optimization techniques may also be used. Commonalgorithms for performing classification include support vector machine(SVM), boosted and bagged decision trees, k-nearest neighbor, NaiveBayes, linear discriminant analysis, logistic regression, and neuralnetworks.

Regression techniques predict continuous responses, for example, changesin temperature or estimates for sales growth. Some applications ofregression techniques include electricity load forecasting andalgorithmic trading. Regression techniques may also use optimizationmethods such as gradient descent or other optimization methods. Commonregression algorithms include linear model, nonlinear model,regularization, stepwise regression, boosted and bagged decision trees,neural networks, and adaptive neuro-fuzzy learning.

Adversarial techniques make use of two networks. One network is used togenerate an output from a first set of data. The second network operatesas a judge to identify if the output data is real or a forgery. Bothnetworks are adjusted during the training process until the firstnetwork can generate outputs that, for example, indistinguishable fromthe real data. Alternative techniques may also be used to train a model.

Classification, regression, and adversarial techniques may be used tosolve problems relating to navigation services. In an example of usingclassification for machine learning training, a method of objectidentification on the roadway involves capturing images as vehiclesdrive around. The images may be annotated to identify objects such asroad markings, traffic signs, other vehicles, and pedestrians forexample. The annotations/labels may be provided by a user or inferred bya user action (e.g. stopping at a stop light). Annotations/labels mayalso be derived from other sensor data (e.g. LIDAR sensor data used tolabel image data). The images are input into a large centralized neuralnetwork that is trained until the neural network reliably recognizes therelevant elements of the images and is able to accurately classify theobjects. A large, disparate set of data is needed to train the neuralnetwork. The process of collecting the large data set of labeled objectsmay run into privacy, bandwidth, and timing issues.

In an embodiment, a machine learned (learnt) model may be trained usingdata from multiple worker devices without sharing data or complicatedtransmission and timing schemes. Each worker device collects data usinga sensor on or about a vehicle. The data may be image data, video data,audio data, text data, personal data, weather data or other types ofdata. In an example of image data collection and object identification,certain objects in the images are labeled based on an existing model,manual annotation, or validation methods. For example, an object in animage may be labeled as a particular sign as the sign exists at thespecified location in a high definition (HD) map database.

Using the labeled objects, each worker device may train a locally storedmodel using a classification technique. Parameters for the locallytrained model are transmitted by each of the worker devices to aparameter server. Due to the disparate data sets and various methods oftransmission, each worker may transmit the parameters asynchronously.Each worker device may, for example, transmit local parameters aftertraining a copy of a local model on one hundred images (differentmetrics may be used). Since the worker devices are capturing differentdata, each worker device may reach one hundred images at differenttimes. A first device finishes training and transmits the parameters tothe parameter server. The parameter server updates a central set ofparameters and transmits the updated central set of parameters back tothe worker device. This process is repeated when each worker deviceasynchronously (e.g. by each device separately and independently)transmits the respective locally generated parameters. Certain workersdevices may transmit local parameters multiple times before anotherworker transmits the respective local parameters. During the processes,the parameter server is constantly updating the central set ofparameters and transmitting the updated set to the worker thattransmitted the local parameters. As workers collect new data, the localmodels may be trained on the new data or a combination of the new andold data. Over time, the transmitted parameters back and forth betweenthe workers and the parameter server eventually settles on a final setof parameters. The final set of parameters and the model may then beused by the worker or other devices to accurately identify objects thatthe devices encounter on the roadway. Other types of models may betrained using the distributed network of devices.

In an embodiment, systems and methods are provided for training a modelusing a gradient descent process on a large number of devices with eachdevice holding a respective piece of training data without sharing datasets. Training using an optimization method such as gradient descentincludes determining how close the model estimates the target function.The determination may be calculated a number of different ways that maybe specific to the particular model being trained. The cost functioninvolves evaluating the parameters in the machine learning model bycalculating a prediction for the model for each training instance in thedataset and comparing the predictions to the actual output values andcalculating an average error value (such as a value of squared residualsor SSR in the case of linear regression). In a simple example of linearregression, a line is fit to a set of points. An error function (alsocalled a cost function) is defined that measures how good (accurate) agiven line is. In an example, the function inputs the points and returnan error value based on how well the line fits the data. To compute theerror for a given line, in this example, each point (x, y) is iteratedin the data set and the sum the square distances between each point's yvalue and the candidate line's y value is calculated as the errorfunction.

Gradient descent is used to minimize the error functions. Given afunction defined by a set of parameters, gradient descent starts with aninitial set of parameter values and iteratively moves toward a set ofparameter values that minimize the function. The iterative minimizationis based on a function that takes steps in the negative direction of thefunction gradient. A search for minimizing parameters starts at anypoint and allows the gradient descent algorithm to proceed downhill onthe error function towards a best outcome. Each iteration updates theparameters that yield a slightly different error than the previousiteration. A learning rate variable is defined that controls how largeof a step that is taken downhill during each iteration.

For image processing and computer vision models, unsupervised learningtechniques may also be used for object detection and image segmentation.Unsupervised learning identifies hidden patterns or intrinsic structuresin the data. Unsupervised learning is used to draw inferences from thedatasets that include input data without labeled responses. One exampleof unsupervised learning technique is clustering. Clustering may be usedto identify patterns or groupings in data. Applications for clusteranalysis may include, for example, gene sequence analysis, marketresearch, and object recognition. Common algorithms for performingclustering include k-means and k-medoids, hierarchical clustering,Gaussian mixture models, hidden Markov models, self-organizing maps,fuzzy c-means clustering, and subtractive clustering. In an embodiment,systems and methods are provided for training an unsupervised machinelearned network on a large number of devices with each device holdingits own piece of training data without sharing data sets.

Unsupervised learning algorithms lack individual target variables andinstead have the goal of characterizing a data set in general.Unsupervised machine learning algorithms are often used to group(cluster) data sets, e.g., to identify relationships between individualdata points (that may include of any number of attributes) and groupthem into clusters. In certain cases, the output from unsupervisedmachine learning algorithms may be used as an input for supervisedmethods. Examples of unsupervised learning include image recognition,forming groups of data based on demographic data, or clustering timeseries to group millions of time series from sensors into groups thatwere previously not obvious.

One problem with training machine learned network is procuring a dataset on which to train the network. The output of a machine learnednetwork may not be accurate if the data on which the network is trainedon is flawed or limited in scope. Collecting a large amount of disparatedata may be curtailed by privacy and transmission concerns. In theexample of object recognition on a roadway, users may be hesitant toprovide personal and local data in mass. Further, raw image data may bemassive and as such difficult to share across a network. Once collected,the data must be processed, required both time and resources.

One solution is to process the data at the devices that collect thedata. In order to facilitate the processing, different methods may beused. One method shares data across devices. Data may be transmitted toa central repository. The data or a model may be transmitted back to theedge devices. This method still includes privacy and transmissionissues. Additionally, the data may be evenly distributed to acceleratethe training. For example, by allocating the same amount or types ofdata to each device, the devices may finish processing the data at orabout the same time allowing a centralized server to capture the resultsat the same time. A centralized server may balance data between devices.

Another solution includes waiting for a certain fraction of devices toreturn before aggregating the learning parameters. Then all the workersare updated based on the aggregated parameters from a subset of nodes.One problem with this solution is that it may depend on having viablebandwidth. The number of devices is also required to specified ahead oftime and the loss or delay of one device may interrupt the learningprocess. For example, if one or more devices are delayed, the entireprocess may also have to wait. Each of these methods has drawbacks asdescribed above. Privacy issues may prohibit transfer of data.Transmission bottlenecks may prohibit or slow transmission to a centralrepository.

Embodiments provide for distributed processing of data while maintainingprivacy and transmission concerns. In an embodiment, all the dataremains on the edge devices to satisfy privacy concerns. No data isavailable centrally to train the model. The ratio of data points todevices may be relatively small resulting in the data on each devicebeing non-independently and identically distributed data (non-I.I.D.)(devices have only a subset of data types) and unbalanced (devices havedifferent orders of magnitude of data). The training occurs in adecentralized manner on multiple devices with only the local dataavailable to each device. The multiple devices do not share data. Theaggregation of model parameters occurs asynchronously on a centralizedparameter server. The aggregation of the model parameters includes asmall linear weighting of the locally-trained model parameters to thecentrally-stored model parameters that is independent of the number ofdata points, the staleness of the parameter updates, and the datadistribution (e.g. unbalanced non-I.I.D.).

FIG. 1 depicts a decentralized system for training a machine learnedmodel. The system includes a plurality of devices 122, a network 127,parameter servers 125, and a mapping platform 121. The mapping platform121 may include or may be connected to a database 123 (also referred toas a geographic database or map database or HD mapping database or HDmap). The mapping platform 121 may include one or more servers 125.Additional, different, or fewer components may be included.

The system includes devices 122 (also referred to as edge devices orworker devices 122). The devices may include probe devices, probesensors, or other devices 122 such as personal navigation devices 122,location aware devices, smart phones mount on a vehicle, or connectedvehicles among other devices. The devices 122 communicate with oneanother using the network 127. Each device 122 may execute softwareconfigured to train a model. Each device 122 may collect and/or storedata relating to the model. The data for each device 122 is notindependently and identically distributed (non-I.I.D.). The distributionof data on two given devices might be quite different. The data for eachdevice 122 is also unbalanced. The amount of data on two given devicesincludes different magnitudes of training data points.

The plurality of devices 122 may include probe devices, probe sensors,or other devices 122 such as personal navigation devices 122 orconnected vehicles. The device 122 may be a navigation system built intothe vehicle and configured to monitor the status of the vehicle. Thedevices 122 may include mobile phones running specialized applicationsthat collect data as the devices 122 are carried by persons or thingstraveling the roadway system. The devices 122 may be configured tocollect and transmit data including the status of a vehicle. The devices122 may be configured to monitor conditions near the vehicle. Thedevices 122 may be configured to provide guidance for a user or vehicle.

The devices 122 may use different sensors such as cameras, lightdetection and ranging (LIDAR), radar, ultrasonic, or other sensors.Different types of data may be collected by a device 122, for example,image data, weather data, vehicular data, audio data, personal data,among others. For example, image data relating to roadways may becollected that represents features such as road lanes, road edges,shoulders, dividers, traffic signals, signage, paint markings, poles,and all other critical data needed for the safe navigation of roadwaysand intersections.

Each of the devices 122 may store a copy of a portion of a geographicdatabase 123 or a full geographic database 123. The geographic database123 may include data for HD mapping. An HD map or HD map data may beprovided to the devices 122 as a cloud-based service. The HD map mayinclude one or more layers. Each layer may offer an additional level ofdetail for accurate and relevant support to connected and autonomousvehicles. The layers may include, for example, a road model, a lanemodel, and a localization model. The road model provides global coveragefor vehicles to identify local insights beyond the range of thevehicle's onboard sensors such as high-occupancy vehicle lanes, orcountry-specific road classification. The lane model may provide moreprecise, lane-level detail such as lane direction of travel, lane type,lane boundary, and lane marking types, to help self-driving vehiclesmake safer and more comfortable driving decisions. The localizationlayer provides support for the vehicle to localize the vehicle in theworld by using roadside objects like guard rails, walls, signs and polelike objects. The vehicle identifies an object, then uses the object'slocation to measure backwards and calculate exactly where the vehicle islocated.

Each of the device 122 may store a model (e.g. machine-learned network)that is trained by a large number (hundreds, thousands, millions, etc.)of devices 122 with each device 122 holding a set of training datawithout sharing data sets. Each device 122 may be configured to traininga pre-agreed model with gradient descent learning for a respective pieceof training data, only sharing learnt parameters of the model with therest of the network. The device 122 is configured to acquire differenttraining data from other devices that are training the model. Inaddition, at least one transmission between the device and a parameterserver occurs asynchronously with respect to the other devices that aretraining the model. The devices 122 may include an HID map that is usedto navigate or provide navigational services. The devices 122 may alsoinclude sensors that capture, for example, image data of features orobject on the roadway. As a device 122 traverses a roadway, the device122 may encounter multiple objects such as other vehicles, cyclists,pedestrians, etc. The device 122 may use the stored model to identify aposition of the vehicle, or the identity of the objects. Based on theidentification, the device 122 may provide navigation instructions ormay provide commands for a vehicle to perform an action.

One or more devices 122 or the mapping platform 127 may be configured asa parameter server 125. The parameter server 125 may also be configureddistinct from the devices 122 or mapping platform 127. The system mayinclude one or more parameter servers 125. The parameter servers 125 areconfigured to receive locally trained model parameters from a device122, adjust centrally stored model parameters, and transmit the adjustedcentrally model parameters back to the device. The parameter server 125communicates with each device 122 of the plurality of devices 122 thatare assigned to the parameter server 125. The parameter servers 125 maybe configured to aggregate parameters from one or more models that aretrained on the devices 122. The parameter servers 125 may be configuredto communicate with devices that are located in a same or similar regionas the parameter server 125. One or more parameter servers 125 maycommunicate with one another. The parameter server 125 is configured tocommunicate asynchronously with the plurality of devices 122. When adevice 122 transmits a set of locally trained model parameters, theparameter server 125 adjusts the central model parameters and transmitsthe adjusted centrally model parameters back to that device. If, forexample, two different devices transmit locally trained modelparameters, the parameter server will perform the adjustment twice, e.g.a first time for the first device that transmitted locally trained modelparameters and then a second time for the second device. The parameterserver does not wait to batch results or average incoming trained modelparameters. Communications between the devices 122 and the parameterserver are one to one and serial, not depending on other communicationwith other devices. Asynchronous communication is the exchange ofmessages between the device and the parameter server responding asschedules permit rather than according to a clock or an event.Communications between each device 122 and parameter server may occurintermittently rather than in a steady stream.

In an embodiment, one or more parameter servers 125 may be configured asa master parameter server. The master parameter server may be configuredto communicate with a plurality of parameter servers; the masterparameter server configured to receive central parameters from theplurality of parameter servers; the master parameter server configuredto calculate and transmit, in response to a communication from theparameter servers of the plurality of parameter servers, a set of globalcentral parameters to a respective parameter server from which thecommunication originated. In an embodiment, the master parameter serveris configured to communicate with both the plurality of parameterservers and the plurality of worker devices.

The parameter server 125 stores a central parameter vector that theparameter server 125 updates each time a device (worker unit) sends aparameter vector to the parameter server 125. A parameter vector may bea collection (e.g. set) of parameters from the model or a representationof the set of parameters. The parameter vector may be a randomly chosencomponents of a parameter vector. Models may include thousands ormillions of parameters. Compressing the set of parameters into aparameter vector may be more efficient for bandwidth and timing thantransmitting and recalculating each parameter of the set of parameters.A parameter vector may also be further compressed. In an embodiment, anincoming parameter vector I may also be compressed into a sparsesubspace vector. For example, if I=(i_1, i_2,i_3 , . . . ,i_n), theincoming parameter vector I may be compressed into I′=(i_b1, i_b2,. . .,i_bm) prior to transmission where m is smaller than n. After receivingI′, at the parameter server, I″ may be uncompressed into I″=(0, 0, i_b1,0, . . . , 0, i_b2, . . . , i_bm,0, . . . ) which is then used as theincoming parameter vector I in Equation 1 described below.

In an embodiment, the update is done using the following equation:

N=(1−α)*O+α*I   EQUATION 1:

where N=the new central parameter vector;

-   O=the old (current) central parameter vector;-   I=the incoming parameter vector;-   Alpha (α)=a fixed real number between 0 and 1;-   * denotes the scalar multiplication; and-   + denotes vector addition.-   The value of alpha may be adjusted automatically or manually    depending on the type of training, the expected number of    iterations, and the number of devices. The value of alpha may be    changed dynamically during the training process. A lower alpha value    discounts the newer incoming parameter, leading to less change in    the central parameter vector. A higher alpha value allows for the    incoming parameters vectors to quickly change the central parameter    vector. The value of alpha may be calculated or set manually or    automatically. The update may also use different functions to    calculate the new central parameter vector. The new central    parameter vector may be calculating using, for example, linear    interpolation.

In an embodiment, the parameter server 125 further communicates withother parameter servers 125. A master parameter server, for example, mayaggregate model parameters from multiple first level parameter servers.The system may be configured with multiple levels of aggregation.Similar to receiving locally trained model parameters, each parameterserver transmits trained model parameters to the master parameter serverand received back master trained model parameters.

In an embodiment, the devices 122 further provide navigation services toan end user or generate commands for vehicular operation. The devices122 may communicate with the mapping platform 127 through the network129. The devices 122 may use trained models (using received parameters)to provide data to assist in identifying a location of the device 122,objects in the vicinity of the device 122, or environmental conditionsaround the device for example.

To provide navigation services, the devices 122 may further receive datafrom the mapping platform 121. The mapping platform 121 may also receivedata from one or more systems or services that may be used to identifythe location of a vehicle, roadway features, or roadway conditions. Thedevice 122 may be configured to acquire and transmit map content data onthe roadway network to the mapping platform 121. As depicted in FIG. 1,the device 122 may be configured to acquire sensor data of a roadwayfeature and the location of the roadway feature (approximation usingpositional circuitry or image processing). The device 122 may beconfigured to identify objects or features in the sensor data using oneor more machine leant models. The device 122 may be configured toidentify the device's location using one or more machine learned models.The one or more machine learned models may be trained on multipledistributed devices on locally stored data that is not shared betweenthe devices. The identified objects or features may be transmitted tothe mapping platform 121 for storage in a geographic database 123. Thegeographic database 123 may be used to provide navigation services tothe plurality of devices 122 and other users.

The mapping platform 121, parameter server 125, and devices 122 areconnected to the network 127. The devices 122 may receive or transmitdata through the network 127 to the other devices 122 or the mappingplatform 127. The mapping platform 121 may receive or transmit datathrough the network 127. The mapping platform 121 may also transmitpaths, routes, or feature data through the network 127. The network 127may include wired networks, wireless networks, or combinations thereof.The wireless network may be a cellular telephone network, LTE (Long-TermEvolution), 4G LTE, a wireless local area network, such as an 802.11,802.16, 802.20, WiMax (Worldwide Interoperability for Microwave Access)network, DSRC (otherwise known as WAVE, ITS-G5, or 802.11p and futuregenerations thereof), a 5G wireless network, or wireless short-rangenetwork. Further, the network 127 may be a public network, such as theInternet, a private network, such as an intranet, or combinationsthereof, and may utilize a variety of networking protocols now availableor later developed including, but not limited to transmission controlprotocol/internet protocol (TCP/IP) based networking protocols.

FIG. 2 depicts an example of a system for training a machine learnedmodel using a plurality of devices. FIG. 2 include three devices thatare configured as worker devices 122 and one device that is configuredas a parameter server 125. Each of the three worker devices 122 includeat least one sensor configured to acquire and store training data. Thethree worker devices 122 communicate with the parameter server 125 usinga communication interface. The parameter server 125 aggregates theparameter vectors from each of the three devices and generates a centralparameter vector. In an embodiment, the aggregation is done usingequation 1 described above. During operation, the three worker devices122 may each include a device processor configured to train a modelusing the training data. The device processor is further configured totransmit a parameter vector of the trained model to a parameter server125. The device processor is further configured to receive an updatedcentral parameter vector from the parameter server 125; the deviceprocessor further configured to retrain the model using the new centralparameter vector. Each of the three devices acquires and storesdifferent training data than other devices. Each of the devicescommunicates with the parameter server 125 asynchronously.

FIG. 3 depicts an example workflow for training a machine learned modelusing a plurality of distributed worker devices 122 such as depicted inFIG. 2. As presented in the following sections, the acts may beperformed using any combination of the components indicated in FIG. 1,FIG. 2, or FIG. 7. The following acts may be performed by the device122, the parameter server 125, the mapping system 121, or a combinationthereof. Additional, different, or fewer acts may be provided. The actsare performed in the order shown or other orders. The acts may also berepeated. Certain acts may be skipped.

By using a plurality of distributed worker devices 122, the model istrained on a much larger volume of data on the edge than can betransferred to a centralized server for bandwidth, privacy, business,and timing reasons. The data, including any personal information,remains on the worker devices 122 and only the model parameters thatencode low- and high-level concepts are shared centrally through aparameter server 125. Since the data stays on the worker devices 122, areduced amount of data is needed to be transferred (e.g. imagedata/audio). Additionally, the model may be trained using a diverse setof data as certain data may not be easily transferred from the devices(for example, automotive sensor data). Finally, as the training occurson the worker devices 122 maintained by third-parties, the cost to runthe large models over huge datasets is at least partially borne by theusers participating in the training process.

At act A110, a worker device 122 trains a model using local trainingdata and a first parameter. The worker device 122 includes a model andlocal training data. The local training data may be data acquired from,for example, a sensor in communication with the worker device 122(camera, LIDAR, microphone, keypad, etc.). The training data on each ofthe devices is not independently and identically distributed(non-I.I.D.). The distribution of data on two given devices is differentand unbalanced (devices have different orders of magnitudes of trainingdata points). In an example, for image data, one device may have severalgigabytes of image data that relates to images taken while traversing ahighway and another device may only have a few megabytes of image dataacquired while traversing a rural road. Both sets of data may be usefulto train an image recognition model even though the sets of data includeimages from two disparate areas and have magnitudes of difference inquantity. The quality of data may also differ between devices. Certaindevices may include higher quality sensors or may include more storagefor data allowing higher quality data to be captured.

In an embodiment, the training data is labeled. Labeled data is used forsupervised learning. The model is trained by imputing known inputs andknown outputs. Weights or parameters are adjusted until the modelaccurately matching the known inputs and output. In an example, to traina machine learned model to identify traffic signs using acquired imagedata, images of traffic signs—with a variety of configurations—arerequired as input variables. In this case, light conditions, angles,soiling, etc. are compiled as noise or blurring in the data as the modelneeds to be able to recognize, for example, a traffic sign in rainyconditions with the same accuracy as when the sun is shining. Thelabels, the correct designations, for such data may be assigned manuallyor automatically. The correct set of input variables and the correctclassifications constitute the training data set.

Labels may be provided by, for example, requesting additional input froma user (requesting a manual annotation), derived from additional data(parsing textual descriptions), or by incorporating additional data fromother sensors. In an example, for a model that identifies location basedfrom image data, the labels for the training set may be provided by aglobal positioning system (GPS) or positional sensor. The model may beused in situations where the GPS sensor is unreliable or in addition tothe GPS sensor. In this scenario, for the training data, the GPS orpositional sensor may be more accurate than locating by imagerecognition. Another example includes training an optical camera torecognize depth using LIDAR as the ground truth, so that the opticalcamera may recognize depth in cars without LIDAR.

Other methods for labeling data may be used, for example, a cloud-basedservice may give accurate, albeit incomplete, labels that be downloadedfrom the cloud to the edge. Delayed user interactions may also providethe label. For example, if a model is attempting to recognize whether astop sign exists a certain intersection, then the behavior of the driver(whether the driver stops at the intersection) may be used to generate alabel for the data.

In an embodiment, the training data is labeled, and the model is taughtusing a supervised learning process. A supervised learning process maybe used to predict numerical values (regression) and for classificationpurposes (predicting the appropriate class). A supervised learningprocessing may include processing images, audio files, videos, numericaldata, and text among other types of data. Classification examplesinclude object recognition (traffic signs, objects in front of avehicle, etc.), face recognition, credit risk assessment, voicerecognition, and customer churn, among others. Regression examplesinclude determining continuous numerical values on the basis of multiple(sometimes hundreds or thousands) input variables, such as aself-driving car calculating the car's ideal speed on the basis of roadand ambient conditions.

The model may be any model that is trained using a machine learnedprocess. The model may include machine learned processes such as supportvector machine (SVM), boosted and bagged decision trees, k-nearestneighbor, Naive Bayes, discriminant analysis, logistic regression, andneural networks. In an example, a two-stage convolutional neural networkis used that includes max pooling layers. The two-stage convolutionalneural network (CNN) uses rectified linear units for the non-linearityand a fully-connected layer at the end for image classification.

In an embodiment, the model may be trained using an adversarial trainingprocess, e.g. the model may include a generative adversarial network(GAN). For an adversarial training approach, a generative network and adiscriminative network are provided for training by the devices. Thegenerative network is trained to identify the features of data in onedomain A and transform the data from domain A into data that isindistinguishable from data in domain B. In the training process, thediscriminative network plays the role of a judge to score how likely thetransformed data from domain A is similar to the data of domain B, e.g.if the data is a forgery or real data from domain B.

In an embodiment, the model is trained using a gradient descenttechnique or a stochastic gradient descent technique. Both techniquesattempt to minimize an error function defined for the model. Fortraining (minimizing the error function), a worker device 122 firstconnects to the parameter server 125. The worker device 122 may startwith randomly initialized model parameters or may request initial modelparameters from the parameter server 125. The starting parameters mayalso be derived from another, pretrained model rather than beingrandomly initialized. The initial parameters may be assigned to allsubsequent edge nodes. Alternatively, updated central parameters may beassigned if the training process has already begun. In an example,worker devices 122 may initially communicate with the parameter server125 at different times. A first device may communicate with theparameter server 125 and be assigned randomly initialized modelparameters. Similarly, a second device may communicate shortlythereafter with the parameter server 125 and be assigned randomlyinitialized model parameters. At some point, devices may begintransmitting local parameters back to the parameter server 125. Theparameter updates the central parameters and transmits the centralparameters back to the respective device. Any device that firstcommunicates with the parameter server 125 after this time may beassigned the central parameters and not the randomly initialized modelparameters. In this way, new devices may be added to the system at anypoint during the training process without disrupting the trainingprocess. Handing out the latest parameters to newly joined edge nodesmay result in faster learning at early stages.

The gradient descent technique attempts to minimize an error functionfor the model. Each device trains a local model using local trainingdata. Training the model involves adjusting internal weights orparameters of the local model until the local model is able toaccurately predict the correct outcome given a newly input data point.The result of the training process is a model that includes one or morelocal parameters that minimize the errors of the function given thelocal training data. The one or more local parameters may be representedas a parameter vector. As the local training data is limited the trainedmodel may not be very accurate when predicting the result of anunidentified input data point. The trained model, however, may betrained to be more accurate given starting parameters that cover a widerswath of data. Better starting parameters may be acquired from theparameter server 125.

Referring back to FIG. 3, at act A120, the worker device 122 transmits asecond parameter from the trained model to the parameter server 125. Thesecond parameter may be parameter vector that is generated as a resultof training the model using the training data. In an embodiment, theworker device 122 may transmit a set of parameters from the model. Agradient, may for example, include thousands or millions of parameters.The set of parameters may be transmitted or compressed in to, forexample, a parameter vector that is transmitted to the parameter server125. In an embodiment, the second parameter set may be a randomly chosensubset of parameters or parameter vectors. The subset may also be, forexample, the second parameter set encoded using a sparsely encodingscheme.

At act A130, the worker device 122 receives a third parameter from theparameter server 125. In an embodiment, the parameter server 125 storesa central parameter vector that the parameter server 125 updates eachtime a worker unit sends it a local parameter or local parameter vector.The parameter server 125 using a weighting function and a weight (Alpha)so that newly received local parameter vectors do not overwhelm thecentral parameter vector. In an embodiment, the parameter server 125updates the central parameter using equation 1 described above. Theupdated central parameter may be transmitted to the device prior to theupdated central parameter being altered again by, for example, anotherdevice requesting a new central parameter. The updating of the centralparameter set by one device may also be decoupled from that same devicegetting back an update. For example, the device may send an updatedlocal parameter set, and then immediately get back the latest centralparameters from the parameter server, without the central parameter sethaving been updated (yet) by the device's local parameters.

The Alpha value may be assigned or adjusted manually depending on thetype of model, number of device, and amount of data. The Alpha value maybe assigned initially and adjust over time or may be static for theentirety of the training process. One method for setting an initialAlpha value is to use a set of test device and benchmark databases. Forexample, two benchmark datasets that may be used to identify an Alphavalue include the Modified National Institute of Standards andTechnology database (MNIST) digit recognition dataset and the CanadianInstitute for Advanced Research (CIFAR-10) dataset. Both datasets may bedistributed with un-even distribution of data, both in terms of the datalabels (restricted to several data labels per node, overlapping andnon-overlapping) and the quantity of data (different orders of magnitudebetween nodes, with some less than the batch size). The test trainingprocess may be run on the test devices to identify an Alpha value thatis correct for the training process given time, bandwidth, and datavolume constraints. A test training process may also identify a qualityof the model. One method for testing is to sample training data fromdevices (e.g. randomly select a training data point from a device beforeit is every used and then remove it from the training data set) andaggregate the samples centrally. Due to privacy concerns, the testingmay only be implemented with user acknowledgement. Another method is tolocally keep a training and testing data set, e.g. randomly chosen foreach data point and, for local training, only local training data isused. After each local training session (certain number of epochs, orother suitably defined iterations) the local test result may be sent toa global test aggregation server that aggregates the test results.

In an embodiment, the Alpha value is set between .01 and .2 indicatingthat new incoming parameters are discounted between 80% and 99% whengenerating the new central parameter vector. Alternative values of Alphamay be used for different processes or models.

At act A140, the worker device 122 retrains the model using the localtraining data and the third parameter. The worker device 122 may use thesame local training data or may update the training data with newlycollected sensor data. The training data may be weighted by age or maybe cycled out by the device. For example, data older than a day, month,or year, may be retired and no longer used for training purposes. Datamay also be removed or deleted by a user or automatically by the device.Additional data may be added to the training data set as the data iscollected.

The model is trained similarly to the act A110. The difference for eachiteration is a different starting point for one or more of theparameters in the model. The central parameter vector that is receivedmay be different than the local parameter vector generated by the devicein A110. At act A150, the worker device 122 transmits the fourthparameter of the updated trained model to the parameter server 125. Atact A160, the worker device 122 receives a fifth parameter from theparameter server 125. The process repeats for a number of iterationuntil the parameters converge or a predetermined number of iteration isreached. This process may be repeated hundreds or thousands of times. Inan example, several thousand (e.g. 3,000 to 5,000) iterations may beperformed. Depending on the complexity of the model and the type andquantity of devices and data, more or fewer iterations may be performed.If new data is added to the training data, the device may retrain themodel and request a new central parameter (and the process may be fullyor partially repeated).

The result of the training process is a model that may be able toaccurately predict the classification given an unlabeled input. At actA170, the model is used on new data to generate, for example, aprediction or classification. In an example, for an image classificationmodel, the worker device 122 identifies an object using the machinelearned model and the fifth parameter.

In an embodiment, an asynchronous learning scheme is provided to train amodel on devices where the data is unbalanced, non-I.I.D, and cannot beshared between devices. In one embodiment, a central parameter server125 receives parameters updates from devices, updates the latest centralparameter state by linear interpolation and then, in turn, immediatelytransmits the latest central parameters to the device. The device inquestion then continues the training regime starting from this newupdated parameter set.

FIG. 2, as described above, depicts three worker devices 122 and aparameter server 125 that may be used for an asynchronous learningscheme. The three worker devices 122 and parameter servers 125 may beany type of device, for example, the device (both worker and parameterserver) may be smartphones, navigation devices, vehicle systems, etc.Each of the worker devices 122 may include a sensor or input interfacethat collects data. Example of sensors may include a camera, LIDAR,radar, microphone, etc. Input interfaces may include, for example, akeyboard or touchscreen. The worker devices 122 locally store data thatacquired using the sensor or input interface. The worker devices 122further store a machine learned model. The machine learn model mayinclude any type of machine learned model.

In the embodiment of FIG. 2, worker units are all implemented asprocesses on distinct devices. Each worker unit is tasked with learninga computational graph model via gradient descent learning, as describedabove. A computational graph model includes a set of nodes where eachnode represents an operation to be performed. The graph model alsoincludes a set of edges or connections between nodes that describe thedata on which the operations is to be performed. Edges may include bothcarriers of data and also control function. A carrier of data describes,for example, where or how the output of one node becomes the input ofanother node. A control function provides a control function, forexample, controlling IF an operation is to be implemented. In acomputational graph model, embodiment, the parameter server 125 isrepresented by a process on another device housing the parameter updatemechanism as described above. The local parameters each device sends arelocally generated model parameters. A worker device first trains thelocally stored model through gradient descent in a pre-arranged fashion(fixed or flexible number of epochs) and then sends the trainedparameters to the device housing the process representing the parameterserver 125. The parameter server 125 calculates an updated parameter andimmediately sends the updated parameter back to the respective device.The parameter server 125 does not wait for additional devices torespond. Upon receipt of the updated parameters, the processrepresenting that worker unit continues its training of the modellocally using local data.

In another embodiment, all units (devices and parameters server) areimplemented as processes on one and the same device, communicating overinternal endpoints, for example, provided by ports in the TransmissionControl Protocol (TCP) protocol. FIG. 4 depicts an embodiment forparameter aggregation contained within a single device 122. Theparameter server 125 is represented by a parameter process 425 thataggregates the central parameter vector as described and each of the oneor more worker units is also represented by a worker process 422, eachof which is tasked with learning a pre-agreed computational graph modelwith gradient descent learning. The parameter vectors sent are a fixedorder of the model parameters of that computational graph model. Anyworker unit process 422 first trains a local model on the data assignedto it. In some implementations, the data assigned to different suchprocesses differs, in some other implementation certain processes sharethe pieces of data. Upon a pre-agreed set of rules (such as trainingsaid model for a precise number of epochs) each process representing aworker unit sends the parameter vector to the parameter process 425representing the parameter server 125, which in turn will update thecentral parameter vector and return it to the sender in question. Theprocess is repeated until the model is trained. The device 122 of FIG. 4may further communicate with other devices 122 or parameter servers 125to further aggregate the parameters.

In another embodiment, the system includes a set of devices that onlyhouse a single worker unit processes each, partitioned into groups, eachof which communicating the respective parameters with a separateparameter server 125 process that is co-located with a processrepresenting a worker unit on a separate device as described in theexample above. FIG. 5 depicts an embodiment for aggregation by ahierarchy of parameters servers. There is not just a single parameterserver 125, but the worker devices 122 and parameter servers 125 havebeen further partitioned into groups. Each parameter server 125 furthertransmits parameters to a master parameter server 525 to be aggregated.

In another embodiment, the parameter server 125 and worker devices 122are established as separate devices as described above, but thearrangement is not hierarchical as in the last example but can usedifferent connections and layouts. FIG. 6 depicts an example ofnon-hierarchal system. Each worker device 122, for example, may be ableto communicate with different parameter servers 125. The parameterservers 125 may be located geographically or may only be able to handlea limited number of connections. Each parameter server 125 may onlyaccept a predefined number of workers after which additional workers areturned away and directed to another parameter server 125. As in theabove described example, the parameter servers 125 may communicate withhigher level parameter servers and so on. A master parameter server 525may communicate with worker devices 122. The parameter servers 125 maycommunicate with one another. Each component (worker device 122,parameter server 125, master parameter server 525) may be configured tofunction as either a worker or a parameter server 125.

FIG. 7 illustrates an example device 122 of the system of FIG. 1. Thedevice 122 may be configured to collect, transmit, receive, process, ordisplay data. The device 122 is configured to train a locally storedmodel using locally stored data in conjunction with other devices 122.The device 122 may also be referred to as a probe 122, a mobile device122, a navigation device 122, or a location aware device 122. Thenavigation device 122 includes a controller 201, a memory 209, an inputdevice 203, a communication interface 205, position circuitry 207, andan output interface 211. The output interface 211 may present visual ornon-visual information such as audio information. Additional, different,or fewer components are possible for the mobile device 122. Thenavigation device 122 may be smart phone, a mobile phone, a personaldigital assistant (PDA), a tablet computer, a notebook computer, apersonal navigation device (PND), a portable navigation device, and/orany other known or later developed mobile device. In an embodiment, avehicle may be considered a device 122, or the device 122 may beintegrated into a vehicle. The device 122 may receive or collect datafrom one or more sensors in or on the vehicle.

The device 122 may be configured to execute routing algorithms using ageographic database 123 to determine an optimum route to travel along aroad network from an origin location to a destination location in ageographic region. Using input from an end user, the device 122 examinespotential routes between the origin location and the destinationlocation to determine the optimum route in light of user preferences orparameters. The device 122 may then provide the end user withinformation about the optimum route in the form of guidance thatidentifies the maneuvers required to be taken by the end user to travelfrom the origin to the destination location. Some devices 122 showdetailed maps on displays outlining the route, the types of maneuvers tobe taken at various locations along the route, locations of certaintypes of features, and so on.

The device 122 is configured to identify a starting location and adestination. The starting location and destination may be identifiedthough the input device 203. The input device 203 may be one or morebuttons, keypad, keyboard, mouse, stylus pen, trackball, rocker switch,touch pad, voice recognition circuit, or other device or component forinputting data to the mobile device 122. The input device 203 and theoutput interface 211 may be combined as a touch screen that may becapacitive or resistive. The output interface 211 may be a liquidcrystal display (LCD) panel, light emitting diode (LED) screen, thinfilm transistor screen, or another type of display. The output interface211 may also include audio capabilities, or speakers.

A positional point may be identified using positional circuitry such asGPS or other positional inputs. The positioning circuitry 207, which isan example of a positioning system, is configured to determine ageographic position of the device 122. In an embodiment, components asdescribed herein with respect to the navigation device 122 may beimplemented as a static device. The navigation device 122 may identify aposition as the device travels along a route using the positionalcircuity. For indoor spaces without GPS signals, the navigation device122 may rely on other geolocations methods such as LIDAR, radar, Wi-Fi,beacons, landmark identification, inertial navigation (dead reckoning),among others.

The device 122 may be configured to acquire data from one or moresensors (not shown). The device 122 may use different sensors such ascameras, microphones, LIDAR, radar, ultrasonic, or other sensors toacquire video, image, text, audio, or other types of data. The acquireddata may be used for training one or more models stored on the device122.

The device 122 may store one or more models in memory 209. The device122 may be configured to train the model using locally acquired data andstore model parameters in the memory 209. The memory 209 may be avolatile memory or a non-volatile memory. The memory 209 may include oneor more of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 209 may be removable from the mobiledevice 122, such as a secure digital (SD) memory card. The memory maycontain a locally stored geographic database 123 or link node routinggraph. The locally stored geographic database 123 may be a copy of thegeographic database 123 or may include a smaller piece. The locallystored geographic database 123 may use the same formatting and scheme asthe geographic database 123. The navigation device 122 may determine aroute or path from a received or locally geographic database 123 usingthe controller 201. The controller 201 may include a general processor,a graphical processing unit (GPU), a digital signal processor, anapplication specific integrated circuit (ASIC), field programmable gatearray (FPGA), analog circuit, digital circuit, combinations thereof, orother now known or later developed processor. The controller 201 may bea single device or combinations of devices, such as associated with anetwork, distributed processing, or cloud computing. The controller 201may also include a decoder used to decode roadway messages and roadwaylocations.

The communication interface 205 may include any operable connection. Anoperable connection may be one in which signals, physicalcommunications, and/or logical communications may be sent and/orreceived. An operable connection may include a physical interface, anelectrical interface, and/or a data interface. The communicationinterface 205 provides for wireless and/or wired communications in anynow known or later developed format. The communication interface 205 mayinclude a receiver/transmitter for digital radio signals or otherbroadcast mediums. The communication interface 205 may be configured tocommunicate model parameters with a parameter server 125.

The navigation device 122 is further configured to request a route fromthe starting location to the destination. The navigation device 122 mayfurther request preferences or information for the route. The navigationdevice 122 may receive updated ambiguity ratings or maps from themapping platform 121 e.g. for geographic regions including the route.The navigation device 122 may communicate with the mapping platform 121or other navigational service using the communication interface 205. Thecommunication interface 205 may include any operable connection. Anoperable connection may be one in which signals, physicalcommunications, and/or logical communications may be sent and/orreceived. An operable connection may include a physical interface, anelectrical interface, and/or a data interface. The communicationinterface 205 provides for wireless and/or wired communications in anynow known or later developed format. The communication interface 205 mayinclude a receiver/transmitter for digital radio signals or otherbroadcast mediums. A receiver/transmitter may be externally located fromthe device 122 such as in or on a vehicle. The route and data associatedwith the route may be displayed using the output interface 211. Theroute may be displayed for example as a top down view or as an isometricprojection.

In certain embodiments, the device 122 may be included in or embodied asan autonomous vehicle. As described herein, an autonomous drivingvehicle may refer to a self-driving or driverless mode that nopassengers are required to be on board to operate the vehicle. Anautonomous driving vehicle may be referred to as a robot vehicle or anautonomous driving vehicle. The autonomous driving vehicle may includepassengers, but no driver is necessary. Autonomous driving vehicles maypark themselves or move cargo between locations without a humanoperator. Autonomous driving vehicles may include multiple modes andtransition between the modes.

As described herein, a highly automated driving (HAD) vehicle may referto a vehicle that does not completely replace the human operator.Instead, in a highly automated driving mode, the vehicle may performsome driving functions and the human operator may perform some drivingfunctions. Vehicles may also be driven in a manual mode that the humanoperator exercises a degree of control over the movement of the vehicle.The vehicles may also include a completely driverless mode. Other levelsof automation are possible.

The autonomous or highly automated driving vehicle may include sensorsfor identifying the surrounding environment and location of the car. Thesensors may include GNSS, light detection and ranging (LIDAR), radar,and cameras for computer vision. Proximity sensors may aid in parkingthe vehicle. The proximity sensors may detect the curb or adjacentvehicles. The autonomous or highly automated driving vehicle mayoptically track and follow lane markings or guide markings on the road.

In an embodiment, the model stored in the device may be used by theautonomous vehicle or navigation system to provide commands orinstructions to the vehicle or user. The model may, for example, assistthe vehicle or navigation system in identifying a position of thevehicle, identifying objects, and determining routes among other complexfunctions.

In an embodiment, the model may be used to determine depth predictionfor car-mounted cameras. The model may predict the distance to objectsaccurately with only access to optical images. The model may be trainedusing local data on multiple devices that included both LIDAR and camerasystems. The model may be deployed on cars that only include camerasystems. The training data would include both the LIDAR data and opticalimages. The model minimization is calculated as the average differencein prediction of depth from camera and LIDAR.

In another embodiment, a model may be trained to estimate the weather ata location of a device based on sensor data. Other devices fromdifferent geographic regions/different sensor configurations may alsolearn to predict the weather. The model parameters are aggregatedwithout sharing data to produce a generalized model. In this example,label of the data may be provided by a cloud-based weather service,downloaded to the devices, in areas with high accuracy in order topredict the weather in areas of poor accuracy/coverage of thecloud-based service. The result will be a highly accurate and generalmodel for weather prediction(estimation) on the device.

In another embodiment, a model may be trained for road sign detection.Training the model using distributed devices allows the model to have ahuge quantity and diversity of data, which allows for a very general andaccurate model to be trained. In another embodiment, a model may betrained to detect open parking spaces.

While the devices may only use local data to train the model or models,the devices may also access data or information from the mappingplatform 121. The additional data from the mapping platform 121 may beused for navigation services or for labeling data in the training datasets.

The mapping platform 121 may include multiple servers, workstations,databases, and other machines connected and maintained by a mapdeveloper. The mapping platform 121 may be configured to receive datafrom devices 122 in the roadway. The mapping platform 121 may beconfigured to identify, verify, and augment features and locations ofthe features from the observational data. The mapping platform 121 maybe configured to update a geographic database 123 with the features andlocations. The mapping platform 121 may be configured to provide featuredata and location data to devices 122. The mapping platform 121 may alsobe configured to generate routes or paths between two points (nodes) ona stored map. The mapping platform 121 may be configured to provide upto date information and maps to external geographic databases 123 ormapping applications. The mapping platform 121 may be configured toencode or decode map or geographic data. Feature data may be stored bythe mapping platform 121 using geographic coordinates such as latitude,longitude, and altitude or other spatial identifiers. The mappingplatform 121 may acquire data relating to the roadway though one or moredevices 122.

The mapping platform 121 may be implemented in a cloud-based computingsystem or a distributed cloud computing service. The mapping platform121 may include one or more server(s). A server may be a host for awebsite or web service such as a mapping service and/or a navigationservice. The mapping service may provide maps generated from thegeographic data of the database 123, and the navigation service maygenerate routing or other directions from the geographic data of thedatabase 123. The mapping service may also provide information generatedfrom attribute data included in the database 123. The server may alsoprovide historical, future, recent or current traffic conditions for thelinks, segments, paths, or routes using historical, recent, or real timecollected data. The server may receive updates from devices 122 orvehicles on the roadway regarding the HD map. The server may generaterouting instructions for devices 122 as a function of HD map updates.

The mapping platform 121 includes the geographic database 123. Toprovide navigation related features and functions to the end user, themapping platform 121 accesses the geographic database 123. The mappingplatform 121 may update or annotate the geographic database 123 with newor changed features based on observational data from the plurality ofdevices 122. The plurality of devices 122 may also store a full orpartial copy of the geographic database 123.

The geographic database 123 includes information about one or moregeographic regions. FIG. 8 illustrates a map of a geographic region 202.The geographic region 202 may correspond to a metropolitan or ruralarea, a state, a country, or combinations thereof, or any other area.Located in the geographic region 202 are physical geographic features,such as roads, points of interest (including businesses, municipalfacilities, etc.), lakes, rivers, railroads, municipalities, etc.

FIG. 8 further depicts an enlarged map 204 of a portion 206 of thegeographic region 202. The enlarged map 204 illustrates part of a roadnetwork 208 in the geographic region 202. The road network 208 includes,among other things, roads and intersections located in the geographicregion 202. As shown in the portion 206, each road in the geographicregion 202 is composed of one or more road segments 210. A road segment210 represents a portion of the road. Each road segment 210 is shown tohave associated with it two nodes 212; one node represents the point atone end of the road segment and the other node represents the point atthe other end of the road segment. The node 212 at either end of a roadsegment 210 may correspond to a location at which the road meets anotherroad, i.e., an intersection, or where the road dead ends.

As depicted in FIG. 9, in one embodiment, the geographic database 123contains geographic data 302 that represents some of the geographicfeatures in the geographic region 202 depicted in FIG. 8. The data 302contained in the geographic database 123 may include data that representthe road network 208. In FIG. 9, the geographic database 123 thatrepresents the geographic region 202 may contain at least one roadsegment database record 304 (also referred to as “entity” or “entry”)for each road segment 210 in the geographic region 202. The geographicdatabase 123 that represents the geographic region 202 may also includea node database record 306 (or “entity” or “entry”) for each node 212 inthe geographic region 202. The terms “nodes” and “segments” representonly one terminology for describing these physical geographic features,and other terminology for describing these features is intended to beencompassed within the scope of these concepts.

The geographic database 123 may include feature data 308-312. Thefeature data 308-312 may represent types of geographic features. Forexample, the feature data may include signage records 308 that identifythe location of signage on the roadway. For example, the signage data308 may include data for one or more signs (e.g. stop signs, yieldsigns, caution signs, etc.) that exist on the roadway network. Thefeature data may include lane features 310 that indicate lane marking onthe roadway. The other kinds of feature data 312 may include point ofinterest data or other roadway features. The point of interest data mayinclude point of interest records comprising a type (e.g., the type ofpoint of interest, such as restaurant, fuel station, hotel, city hall,police station, historical marker, ATM, golf course, truck stop, vehiclechain-up stations etc.), location of the point of interest, a phonenumber, hours of operation, etc. The feature data may also includepainted signs on the road, traffic signal, physical and painted featureslike dividers, lane divider markings, road edges, center ofintersection, stop bars, overpasses, overhead bridges etc. The featuredata may be identified from data received by the devices 122. More,fewer or different data records can be provided. In one embodiment,additional data records (not shown) can include cartographic (“carto”)data records, routing data, and maneuver data.

The feature data 308-312 may include HD mapping data that may model roadsurfaces and other map features to decimeter or centimeter-level orbetter accuracy. An HD map database may include locations data in threedimensions with a spatial resolution of at least a threshold distance topixel ratio. Example threshold distance ratios include 30 centimetersper pixel (i.e., each pixel in the image for the HD map represents 30centimeters in the three-dimensional space), 20 centimeters per pixel,or other values. The HD maps may be defined according to the Open LaneModel of the Navigation Data Standard (NDS). The feature data 308-312may also include lane models that provide the precise lane geometry withlane boundaries, as well as rich attributes of the lane models. The richattributes include, but are not limited to, lane traversal information,lane types, lane marking types, lane level speed limit information,and/or the like. In one embodiment, the feature data 308-312 are dividedinto spatial partitions of varying sizes to provide HD mapping data tovehicles 101 and other end user devices 122 with near real-time speedwithout overloading the available resources of the devices 122 (e.g.,computational, memory, bandwidth, etc. resources). The feature data308-312 may be created from high-resolution 3D mesh or point-cloud datagenerated, for instance, from LIDAR-equipped vehicles. The 3D mesh orpoint-cloud data are processed to create 3D representations of a streetor geographic environment at decimeter or centimeter-level accuracy forstorage in the feature data 308-312. The feature data 308-312 may alsoinclude data the is useful for machine learning or computer vision, butnot readily attribution to easy categorization as human-recognizablefeatures.

In an embodiment, the feature data 308-312 also include real-time sensordata collected from probe vehicles in the field. The real-time sensordata, for instance, integrates real-time road event data, trafficinformation, weather, and road conditions (e.g., potholes, roadfriction, road wear, etc.) with highly detailed 3D representations ofstreet and geographic features to provide precise real-time featuredetection at decimeter or centimeter-level accuracy. Other sensor datacan include vehicle telemetry or operational data such as windshieldwiper activation state, braking state, steering angle, acceleratorposition, and/or the like.

The geographic database 123 also includes indexes 314. The indexes 314may include various types of indexes that relate the different types ofdata to each other or that relate to other aspects of the data containedin the geographic database 123. For example, the indexes 314 may relatethe nodes in the node data records 306 with the end points of a roadsegment in the road segment data records 304. As another example, theindexes 314 may relate feature data such as the signage records 308 witha road segment in the segment data records 304 or a geographiccoordinate. The indexes 314 may also store repeating geometry patternsor relationships for links or nodes that represent repeating geometrypatterns.

The geographic database 123 may be maintained by a content provider(e.g., a map developer). By way of example, the map developer maycollect geographic data to generate and enhance the geographic database123. The map developer may obtain data from sources, such as businesses,municipalities, or respective geographic authorities. In addition, themap developer may employ field personnel to travel throughout thegeographic region to observe features and/or record information aboutthe roadway. Also, remote sensing, such as aerial or satellitephotography, can be used.

The geographic database 123 and the data stored within the geographicdatabase 123 may be licensed or delivered on-demand. Other navigationalservices or traffic server providers may access the traffic data and theregulatory data stored in the geographic database 123. Data includingregulation data may be broadcast as a service.

The term “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding, or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom-access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, GPUs programmable logicarrays and other hardware devices, can be constructed to implement oneor more of the methods described herein. Applications that may includethe apparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in the specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in the application, the term ‘circuitry’ or ‘circuit’ refers toall of the following: (a)hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of ‘circuitry’ applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom-access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a GPS receiver, to name just a few. Computerreadable media suitable for storing computer program instructions anddata include all forms of non-volatile memory, media, and memorydevices, including by way of example semiconductor memory devices, e.g.,EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internalhard disks or removable disks; magneto optical disks; and CD ROM andDVD-ROM disks. The memory may be a non-transitory medium such as a ROM,RAM, flash memory, etc. The processor and the memory can be supplementedby, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, are apparent to those of skill in the artupon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

The following embodiments are disclosed. Embodiment 1: A navigationdevice for training a machine learned model, the device comprising atleast one sensor configured to acquire training data; a communicationinterface configured to communicate with a parameter server; and adevice processor configured to train the machine learned model using thetraining data; the device processor further configured to transmit aparameter vector of the trained model to the parameter server andreceive in response, an updated central parameter vector from theparameter server; the device processor further configured to retrain themodel using the updated central parameter vector; wherein the navigationdevice acquires different training data from other devices that aretraining the model; wherein at least one transmission between thenavigation device and the parameter server occur asynchronously withrespect to the other devices that are training the model.

Embodiment 2: the navigation device of embodiment 1, wherein thetraining data is image data, and the machine learned model is trained toidentify a position of the navigation device.

Embodiment 3: the navigation device of embodiment 1, wherein thetraining data is image data, and the machine learned model is trained toidentify an object in the image data.

Embodiment 4: the navigation device of embodiment 1, wherein trainingthe machine learned model includes a gradient descent-based process.

Embodiment 5: the navigation device of embodiment 1, wherein the atleast one sensor is embedded in a vehicle.

Embodiment 6: the navigation device of embodiment 1, wherein the machinelearned model comprises a generative adversarial network, wherein thedevice processor is configured to train the machine learnt model usingan adversarial training process.

Embodiment 7: the navigation device of embodiment 1, wherein thenavigation device acquires different amounts of training data from theother devices.

Embodiment 8: the navigation device of embodiment 1, wherein the updatedcentral parameter vector is updated by the parameter server as afunction of a weighting value.

Embodiment 9: the navigation device of embodiment 1, wherein theparameter vector comprises a randomly selected subset of parameters ofthe trained model.

Embodiment 10: the navigation device of embodiment 1, wherein thetraining data is labeled, and the machine learned model is trained usinga supervised training process.

Embodiment 11: the navigation device of embodiment 1, wherein theupdated central parameter is transmitted to the device prior to theupdated central parameter being altered again.

Embodiment 12: a method for training a machine learned model using aplurality of distributed worker devices, the method comprising:training, by a worker device, a machine learned model using localtraining data and a set of first parameters; transmitting, by the workerdevice, a set of second parameters of the trained machine learned modelto a parameter server; receiving, by the worker device, a set of thirdparameters from the parameter server, wherein the set of thirdparameters is calculated at least partially as a function of the set ofsecond parameters; and training, by the worker device the machinelearned model using the local training data and the set of thirdparameters.

Embodiment 13: the method of embodiment 12, further comprising:transmitting, by the work device, a set of fourth parameters of thetrained machine learned model to the parameter server.

Embodiment 14: the method of embodiment 12, wherein the third parameteris received asynchronously from the parameter server.

Embodiment 15: the method of embodiment 12, wherein the set of firstparameters comprises a randomly assigned set of values.

Embodiment 16: the method of embodiment 12, wherein the local trainingdata is accessible only on the worker device.

Embodiment 17: the method of embodiment 12, wherein the local trainingdata is image data and the machine learned model is an image recognitionmachine learned model.

Embodiment 18: the method of embodiment 17, further comprising:acquiring, by the worker device, new image data; and identifying, by theworker device, an object in the new image data using the trained machinelearned model.

Embodiment 19: the method of embodiment 12, further comprising:determining a randomly chosen subset of parameters of the set of secondparameters; wherein the randomly chosen subset of parameters istransmitted to the parameter server in place of the set of secondparameters

Embodiment 20: a system for training a machine learned model, the systemcomprising: a parameter server configured to communicate with theplurality of worker devices; the parameter server configured to receivelocally generated sets of parameters of the trained machine learnedmodels from the plurality of worker devices; the parameter serverconfigured to calculate and transmit, in response to a communicationfrom a worker device of the plurality of worker devices, a set ofcentral parameters to the respective worker device from which thecommunication originated. The plurality of worker devices are configuredto train the machine learned model using a set of parameters andrespective sets of locally acquired training data.

Embodiment 21: the system of embodiment 20, further comprising a masterparameter server configured to communicate with a plurality of parameterservers; the master parameter server configured to receive centralparameters from the plurality of parameter servers; the master parameterserver configured to calculate and transmit, in response to acommunication from the parameter servers of the plurality of parameterservers , a set of global central parameters to a respective parameterserver from which the communication originated.

Embodiment 22: the system of embodiment 20, wherein the respective setsof locally acquired training data are only accessible by the respectiveworker devices of the plurality of worker devices.

Embodiment 23: the system of embodiment 20, wherein the parameter serveris configured to communicate asynchronously with the plurality of workerdevices.

Embodiment 24: the system of embodiment 21, wherein the master parameterserver is configured to communicate with both the plurality of parameterservers and the plurality of worker devices.

1. A navigation device for training a machine learned model, the devicecomprising at least one sensor configured to acquire training data; acommunication interface configured to communicate with a parameterserver; and a device processor configured to train the machine learnedmodel using the training data; the device processor further configuredto transmit a parameter vector of the trained model to the parameterserver and receive in response, an updated central parameter vector fromthe parameter server; the device processor further configured to retrainthe model using the updated central parameter vector; wherein thenavigation device acquires different training data from other devicesthat are training the model; wherein at least one transmission betweenthe navigation device and the parameter server occur asynchronously withrespect to the other devices that are training the model.
 2. Thenavigation device of claim 1, wherein the training data is image data,and the machine learned model is trained to identify a position of thenavigation device.
 3. The navigation device of claim 1, wherein thetraining data is image data, and the machine learned model is trained toidentify an object in the image data.
 4. The navigation device of claim1, wherein training the machine learned model includes a gradientdescent-based process.
 5. The navigation device of claim 1, wherein theat least one sensor is embedded in a vehicle.
 6. The navigation deviceof claim 1, wherein the machine learned model comprises a generativeadversarial network, wherein the device processor is configured to trainthe machine learnt model using an adversarial training process.
 7. Thenavigation device of claim 1, wherein the navigation device acquiresdifferent amounts of training data from the other devices.
 8. Thenavigation device of claim 1, wherein the updated central parametervector is updated by the parameter server as a function of a weightingvalue.
 9. The navigation device of claim 1, wherein the parameter vectorcomprises a randomly selected subset of parameters of the trained model.10. The navigation device of claim 1, wherein the training data islabeled, and the machine learned model is trained using a supervisedtraining process.
 11. The navigation device of claim 1, wherein theupdated central parameter is transmitted to the device prior to theupdated central parameter being altered again.
 12. A method for traininga machine learned model using a plurality of distributed worker devices,the method comprising: training, by a worker device, a machine learnedmodel using local training data and a set of first parameters;transmitting, by the worker device, a set of second parameters of thetrained machine learned model to a parameter server; receiving, by theworker device, a set of third parameters from the parameter server,wherein the set of third parameters is calculated at least partially asa function of the set of second parameters; and training, by the workerdevice the machine learned model using the local training data and theset of third parameters.
 13. The method of claim 12, further comprising:transmitting, by the work device, a set of fourth parameters of thetrained machine learned model to the parameter server.
 14. The method ofclaim 12, wherein the third parameter is received asynchronously fromthe parameter server.
 15. The method of claim 12, wherein the set offirst parameters comprises a randomly assigned set of values.
 16. Themethod of claim 12, wherein the local training data is accessible onlyon the worker device.
 17. The method of claim 12, wherein the localtraining data is image data and the machine learned model is an imagerecognition machine learned model.
 18. The method of claim 17, furthercomprising: acquiring, by the worker device, new image data; andidentifying, by the worker device, an object in the new image data usingthe trained machine learned model.
 19. The method of claim 12, furthercomprising: determining a randomly chosen subset of parameters of theset of second parameters; wherein the randomly chosen subset ofparameters is transmitted to the parameter server in place of the set ofsecond parameters.
 20. A system for training a machine learned model,the system comprising: a parameter server configured to communicate witha plurality of worker devices; the parameter server configured toreceive locally generated sets of parameters of the trained machinelearned models from the plurality of worker devices; the parameterserver configured to calculate and transmit, in response to acommunication from a worker device of the plurality of worker devices, aset of central parameters to the respective worker device from which thecommunication originated; wherein the plurality of worker devices areconfigured to train the machine learned model using the set of centralparameters and respective sets of locally acquired training data. 21.The system of claim 20, further comprising a master parameter serverconfigured to communicate with a plurality of parameter servers; themaster parameter server configured to receive central parameters fromthe plurality of parameter servers; the master parameter serverconfigured to calculate and transmit, in response to a communicationfrom the parameter servers of the plurality of parameter servers , a setof global central parameters to a respective parameter server from whichthe communication originated.
 22. The system of claim 20, wherein therespective sets of locally acquired training data are only accessible bythe respective worker devices of the plurality of worker devices. 23.The system of claim 20, wherein the parameter server is configured tocommunicate asynchronously with the plurality of worker devices.
 24. Thesystem of claim 21, wherein the master parameter server is configured tocommunicate with both the plurality of parameter servers and theplurality of worker devices.